PubMed

Sci Rep. 2024 May 21;14(1):11549. doi: 10.1038/s41598-024-62518-w.ABSTRACTPediatric chronic kidney disease (CKD) is a clinical condition characterized by progressive renal function deterioration. CKD diagnosis is based on glomerular filtration rate, but its reliability is limited, especially at the early stages. New potential biomarkers (citrulline (CIT), symmetric dimethylarginine (SDMA), S-adenosylmethionine (SAM), n-butyrylcarnitine (nC4), cis-4-decenoylcarnitine, sphingosine-1-phosphate and bilirubin) in addition to creatinine (CNN) have been proposed for early diagnosis. To verify the clinical value of these biomarkers we performed a comprehensive targeted metabolomics study on a representative cohort of CKD and healthy pediatric patients. Sixty-seven children with CKD and forty-five healthy children have been enrolled in the study. Targeted metabolomics based on liquid chromatography-triple quadrupole mass spectrometry has been used for serum and plasma samples analysis. Univariate data analysis showed statistically significant differences (p < 0.05) in the concentration of CNN, CIT, SDMA, and nC4 among healthy and CKD pediatric patients. The predictive ability of the proposed biomarkers was also confirmed through specificity and sensitivity expressed in Receiver Operating Characteristic curves (AUC = 0.909). In the group of early CKD pediatric patients, AUC of 0.831 was obtained, improving the diagnostic reliability of CNN alone. Moreover, the models built on combined CIT, nC4, SDMA, and CNN allowed to distinguish CKD patients from healthy control regardless of blood matrix type (serum or plasma). Our data demonstrate potential biomarkers in the diagnosis of early CKD stages.PMID:38773318 | DOI:10.1038/s41598-024-62518-w

Sci Rep. 2024 May 21;14(1):11585. doi: 10.1038/s41598-024-62360-0.ABSTRACTHigh-altitude cerebral edema (HACE) is a severe neurological condition that can occur at high altitudes. It is characterized by the accumulation of fluid in the brain, leading to a range of symptoms, including severe headache, confusion, loss of coordination, and even coma and death. Exosomes play a crucial role in intercellular communication, and their contents have been found to change in various diseases. This study analyzed the metabolomic characteristics of blood exosomes from HACE patients compared to those from healthy controls (HCs) with the aim of identifying specific metabolites or metabolic pathways associated with the development of HACE conditions. A total of 21 HACE patients and 21 healthy controls were recruited for this study. Comprehensive metabolomic profiling of the serum exosome samples was conducted using ultraperformance liquid chromatography-tandem mass spectrometry (UPLC‒MS/MS). Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to identify the metabolic pathways affected in HACE patients. Twenty-six metabolites, including ( +)-camphoric acid, choline, adenosine, adenosine 5'-monophosphate, deoxyguanosine 5'-monophosphate, guanosine, and hypoxanthine-9-β-D-arabinofuranoside, among others, exhibited significant changes in expression in HACE patients compared to HCs. Additionally, these differentially abundant metabolites were confirmed to be potential biomarkers for HACE. KEGG pathway enrichment analysis revealed several pathways that significantly affect energy metabolism regulation (such as purine metabolism, thermogenesis, and nucleotide metabolism), estrogen-related pathways (the estrogen signaling pathway, GnRH signaling pathway, and GnRH pathway), cyclic nucleotide signaling pathways (the cGMP-PKG signaling pathway and cAMP signaling pathway), and hormone synthesis and secretion pathways (renin secretion, parathyroid hormone synthesis, secretion and action, and aldosterone synthesis and secretion). In patients with HACE, adenosine, guanosine, and hypoxanthine-9-β-D-arabinofuranoside were negatively correlated with height. Deoxyguanosine 5'-monophosphate is negatively correlated with weight and BMI. Additionally, LPE (18:2/0:0) and pregnanetriol were positively correlated with age. This study identified potential biomarkers for HACE and provided valuable insights into the underlying metabolic mechanisms of this disease. These findings may lead to potential targets for early diagnosis and therapeutic intervention in HACE patients.PMID:38773195 | DOI:10.1038/s41598-024-62360-0

Sci Rep. 2024 May 21;14(1):11551. doi: 10.1038/s41598-024-58160-1.ABSTRACTMetabolic disorder has been found to be an important factor in the pathogenesis and progression of sepsis. However, the causation of such an association between serum metabolites and sepsis has not been established. We conducted a two-sample Mendelian randomization (MR) study. A genome-wide association study of 486 human serum metabolites was used as the exposure, whereas sepsis and sepsis mortality within 28 days were set as the outcomes. In MR analysis, 6 serum metabolites were identified to be associated with an increased risk of sepsis, and 6 serum metabolites were found to be related to a reduced risk of sepsis. Furthermore, there were 9 metabolites positively associated with sepsis-related mortality, and 8 metabolites were negatively correlated with sepsis mortality. In addition, "glycolysis/gluconeogenesis" (p = 0.001), and "pyruvate metabolism" (p = 0.042) two metabolic pathways were associated with the incidence of sepsis. This MR study suggested that serum metabolites played significant roles in the pathogenesis of sepsis, which may provide helpful biomarkers for early disease diagnosis, therapeutic interventions, and prognostic assessments for sepsis.PMID:38773119 | DOI:10.1038/s41598-024-58160-1

NPJ Parkinsons Dis. 2024 May 21;10(1):106. doi: 10.1038/s41531-024-00724-z.ABSTRACTWe aimed to identify gut microbial features in Parkinson's disease (PD) across countries by meta-analyzing our fecal shotgun sequencing dataset of 94 PD patients and 73 controls in Japan with five previously reported datasets from USA, Germany, China1, China2, and Taiwan. GC-MS and LC-MS/MS assays were established to quantify fecal short-chain fatty acids (SCFAs) and fecal polyamines, respectively. α-Diversity was increased in PD across six datasets. Taxonomic analysis showed that species Akkermansia muciniphila was increased in PD, while species Roseburia intestinalis and Faecalibacterium prausnitzii were decreased in PD. Pathway analysis showed that genes in the biosyntheses of riboflavin and biotin were markedly decreased in PD after adjusting for confounding factors. Five out of six categories in carbohydrate-active enzymes (CAZymes) were decreased in PD. Metabolomic analysis of our fecal samples revealed that fecal SCFAs and polyamines were significantly decreased in PD. Genes in the riboflavin and biotin biosyntheses were positively correlated with the fecal concentrations of SCFAs and polyamines. Bacteria that accounted for the decreased riboflavin biosynthesis in Japan, the USA, and Germany were different from those in China1, China2, and Taiwan. Similarly, different bacteria accounted for decreased biotin biosynthesis in the two country groups. We postulate that decreased SCFAs and polyamines reduce the intestinal mucus layer, which subsequently facilitates the formation of abnormal α-synuclein fibrils in the intestinal neural plexus in PD, and also cause neuroinflammation in PD.PMID:38773112 | DOI:10.1038/s41531-024-00724-z

Metabolomics. 2024 May 21;20(3):58. doi: 10.1007/s11306-024-02131-0.ABSTRACTINTRODUCTION: Bio stimulants are substances and/or microorganisms that are used to improve plant growth and crop yields by modulating physiological processes and metabolism of plants. While research has primarily focused on the broad effects of bio stimulants in crops, understanding their cellular and molecular influences in plants, using metabolomic analysis, could elucidate their effectiveness and offer possibilities for fine-tuning their application. One such bio stimulant containing galacturonic acid as elicitor is used in agriculture to improve wheat vigor and strengthen resistance to lodging.OBJECTIVE: However, whether a metabolic response is evolved by plants treated with this bio stimulant and the manner in which the latter might regulate plant metabolism have not been studied.METHOD: Therefore, the present study used 1H-NMR and LC-MS to assess changes in primary and secondary metabolites in the roots, stems, and leaves of wheat (Triticum aestivum) treated with the bio stimulant. Orthogonal partial least squares discriminant analysis effectively distinguished between treated and control samples, confirming a metabolic response to treatment in the roots, stems, and leaves of wheat.RESULTS: Fold-change analysis indicated that treatment with the bio stimulation solution appeared to increase the levels of hydroxycinnamic acid amides, lignin, and flavonoid metabolism in different plant parts, potentially promoting root growth, implantation, and developmental cell wall maturation and lignification.CONCLUSION: These results demonstrate how non-targeted metabolomic approaches can be utilized to investigate and monitor the effects of new agroecological solutions based on systemic responses.PMID:38773056 | DOI:10.1007/s11306-024-02131-0

Metabolomics. 2024 May 21;20(3):57. doi: 10.1007/s11306-024-02118-x.ABSTRACTBACKGROUND: Despite the clear clinical diagnostic criteria for necrozoospermia in andrology, the fundamental mechanisms underlying it remain elusive. This study aims to profile the lipid composition in seminal plasma systematically and to ascertain the potential of lipid biomarkers in the accurate diagnosis of necrozoospermia. It also evaluates the efficacy of a lipidomics-based random forest algorithm model in identifying necrozoospermia.METHODS: Seminal plasma samples were collected from patients diagnosed with necrozoospermia (n = 28) and normozoospermia (n = 28). Liquid chromatography-mass spectrometry (LC-MS) was used to perform lipidomic analysis and identify the underlying biomarkers. A lipid functional enrichment analysis was conducted using the LION lipid ontology database. The top 100 differentially significant lipids were subjected to lipid biomarker examination through random forest machine learning model.RESULTS: Lipidomic analysis identified 46 lipid classes comprising 1267 lipid metabolites in seminal plasma. The top five enriched lipid functions as follows: fatty acid (FA) with ≤ 18 carbons, FA with 16-18 carbons, monounsaturated FA, FA with 18 carbons, and FA with 16 carbons. The top 100 differentially significant lipids were subjected to machine learning analysis and identified 20 feature lipids. The random forest model identified lipids with an area under the curve > 0.8, including LPE(20:4) and TG(4:0_14:1_16:0).CONCLUSIONS: LPE(20:4) and TG(4:0_14:1_16:0), were identified as differential lipids for necrozoospermia. Seminal plasma lipidomic analysis could provide valuable biochemical information for the diagnosis of necrozoospermia, and its combination with conventional sperm analysis may improve the accuracy and reliability of the diagnosis.PMID:38773045 | DOI:10.1007/s11306-024-02118-x

Metabolomics. 2024 May 21;20(3):59. doi: 10.1007/s11306-024-02119-w.ABSTRACTINTRODUCTION: Thyroid cancer incidence rate has increased substantially worldwide in recent years. Fine needle aspiration biopsy (FNAB) is currently the golden standard of thyroid cancer diagnosis, which however, is invasive and costly. In contrast, breath analysis is a non-invasive, safe and simple sampling method combined with a promising metabolomics approach, which is suitable for early cancer diagnosis in high volume population.OBJECTIVES: This study aims to achieve a more comprehensive and definitive exhaled breath metabolism profile in papillary thyroid cancer patients (PTCs).METHODS: We studied both end-tidal and mixed expiratory breath, solid-phase microextraction gas chromatography coupled with high resolution mass spectrometry (SPME-GC-HRMS) was used to analyze the breath samples. Multivariate combined univariate analysis was applied to identify potential breath biomarkers.RESULTS: The biomarkers identified in end-tidal and mixed expiratory breath mainly included alkanes, olefins, enols, enones, esters, aromatic compounds, and fluorine and chlorine containing organic compounds. The area under the curve (AUC) values of combined biomarkers were 0.974 (sensitivity: 96.1%, specificity: 90.2%) and 0.909 (sensitivity: 98.0%, specificity: 74.5%), respectively, for the end-tidal and mixed expiratory breath, indicating of reliability of the sampling and analysis method CONCLUSION: This work not only successfully established a standard metabolomic approach for early diagnosis of PTC, but also revealed the necessity of using both the two breath types for comprehensive analysis of the biomarkers.PMID:38773019 | DOI:10.1007/s11306-024-02119-w

Metabolomics. 2024 May 21;20(3):60. doi: 10.1007/s11306-024-02111-4.ABSTRACTMetabolomic epidemiology studies are complex and require a broad array of domain expertise. Although many metabolite-phenotype associations have been identified; to date, few findings have been translated to the clinic. Bridging this gap requires understanding of both the underlying biology of these associations and their potential clinical implications, necessitating an interdisciplinary team approach. To address this need in metabolomic epidemiology, a workshop was held at Metabolomics 2023 in Niagara Falls, Ontario, Canada that highlighted the domain expertise needed to effectively conduct these studies -- biochemistry, clinical science, epidemiology, and assay development for biomarker validation -- and emphasized the role of interdisciplinary teams to move findings towards clinical translation.PMID:38773013 | DOI:10.1007/s11306-024-02111-4

Diabetologia. 2024 May 21. doi: 10.1007/s00125-024-06169-6. Online ahead of print.ABSTRACTAIMS/HYPOTHESIS: Many studies have examined the relationship between plasma metabolites and type 2 diabetes progression, but few have explored saliva and multi-fluid metabolites.METHODS: We used LC/MS to measure plasma (n=1051) and saliva (n=635) metabolites among Puerto Rican adults from the San Juan Overweight Adults Longitudinal Study. We used elastic net regression to identify plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting baseline HOMA-IR in a training set (n=509) and validated these scores in a testing set (n=340). We used multivariable Cox proportional hazards models to estimate HRs for the association of baseline metabolomic scores predicting insulin resistance with incident type 2 diabetes (n=54) and prediabetes (characterised by impaired glucose tolerance, impaired fasting glucose and/or high HbA1c) (n=130) at 3 years, along with regression from prediabetes to normoglycaemia (n=122), adjusting for traditional diabetes-related risk factors.RESULTS: Plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting insulin resistance included highly weighted metabolites from fructose, tyrosine, lipid and amino acid metabolism. Each SD increase in the plasma (HR 1.99 [95% CI 1.18, 3.38]; p=0.01) and multi-fluid (1.80 [1.06, 3.07]; p=0.03) metabolomic scores was associated with higher risk of type 2 diabetes. The saliva metabolomic score was associated with incident prediabetes (1.48 [1.17, 1.86]; p=0.001). All three metabolomic scores were significantly associated with lower likelihood of regressing from prediabetes to normoglycaemia in models adjusting for adiposity (HRs 0.72 for plasma, 0.78 for saliva and 0.72 for multi-fluid), but associations were attenuated when adjusting for lipid and glycaemic measures.CONCLUSIONS/INTERPRETATION: The plasma metabolomic score predicting insulin resistance was more strongly associated with incident type 2 diabetes than the saliva metabolomic score. Only the saliva metabolomic score was associated with incident prediabetes.PMID:38772919 | DOI:10.1007/s00125-024-06169-6

J Sep Sci. 2024 May;47(9-10):e2400155. doi: 10.1002/jssc.202400155.ABSTRACTRapid evaporative ionization mass spectrometry (REIMS) is a relatively recent MS technique explored in many application fields, demonstrating high versatility in the detection of a wide range of chemicals, from small molecules (phenols, amino acids, di- and tripeptides, organic acids, and sugars) to larger biomolecules, that is, phospholipids and triacylglycerols. Different sampling devices were used depending on the analyzed matrix (liquid or solid), resulting in distinct performances in terms of automation, reproducibility, and sensitivity. The absence of laborious and time-consuming sample preparation procedures and chromatographic separations was highlighted as a major advantage compared to chromatographic methods. REIMS was successfully used to achieve a comprehensive sample profiling according to a metabolomics untargeted analysis. Moreover, when a multitude of samples were available, the combination with chemometrics allowed rapid sample differentiation and the identification of discriminant features. The present review aims to provide a survey of literature reports based on the use of such analytical technology, highlighting its mode of operation in different application areas, ranging from clinical research, mostly focused on cancer diagnosis for the accurate identification of tumor margins, to the agri-food sector aiming at the safeguard of food quality and security.PMID:38772742 | DOI:10.1002/jssc.202400155

Ann N Y Acad Sci. 2024 May 21. doi: 10.1111/nyas.15147. Online ahead of print.ABSTRACTAmyotrophic lateral sclerosis (ALS) is a devastating motor neuron disease. The immunosuppressive functions of regulatory T lymphocytes (Tregs) are impaired in ALS, and correlate to disease progression. The phase 2a IMODALS trial reported an increase in Treg number in ALS patients following the administration of low-dose (ld) interleukin-2 (IL-2). We propose a pharmacometabolomics approach to decipher metabolic modifications occurring in patients treated with ld-IL-2 and its relationship with Treg response. Blood metabolomic profiles were determined on days D1, D64, and D85 from patients receiving 2 MIU of IL-2 (n = 12) and patients receiving a placebo (n = 12). We discriminated the three time points for the treatment group (average error rate of 42%). Among the important metabolites, kynurenine increased between D1 and D64, followed by a reduction at D85. The percentage increase of Treg number from D1 to D64, as predicted by the metabolome at D1, was highly correlated with the observed value. This study provided a proof of concept for metabolic characterization of the effect of ld-IL-2 in ALS. These data could present advances toward a personalized medicine approach and present pharmacometabolomics as a key tool to complement genomic and transcriptional data for drug characterization, leading to systems pharmacology.PMID:38771698 | DOI:10.1111/nyas.15147

Plant Physiol. 2024 May 21:kiae287. doi: 10.1093/plphys/kiae287. Online ahead of print.ABSTRACTLignin is a phenolic polymer in plants that rigidifies the cell walls of water-conducting tracheary elements and support-providing fibers and stone cells. Different mechanisms have been suggested for the transport of lignin precursors to the site of lignification in the cell wall. Extracellular vesicle (EV)-enriched samples isolated from a lignin-forming cell suspension culture of Norway spruce (Picea abies L. Karst.) contained both phenolic metabolites and enzymes related to lignin biosynthesis. Metabolomic analysis revealed mono-, di- and oligolignols in the EV isolates, as well as carbohydrates and amino acids. In addition, salicylic acid (SA) and some proteins involved in SA signaling were detected in the EV-enriched samples. A proteomic analysis detected several laccases, peroxidases, β-glucosidases, putative dirigent proteins, and cell wall-modifying enzymes such as glycosyl hydrolases, transglucosylase/hydrolases, and expansins in EVs. Our findings suggest that EVs are involved in transporting enzymes required for lignin polymerization in Norway spruce, and that radical coupling of monolignols can occur in these vesicles.PMID:38771246 | DOI:10.1093/plphys/kiae287

Clin Transl Sci. 2024 May;17(5):e13834. doi: 10.1111/cts.13834.ABSTRACTPioglitazone is class of thiazolidinediones that activates peroxisome proliferator-activated receptors (PPARs) in adipocytes to improve glucose metabolism and insulin sensitivity and has been used as a treatment for type 2 diabetes. However, the underlying mechanisms of associated pioglitazone-induced effects remain unclear. Our study aimed to investigate endogenous metabolite alterations associated with pioglitazone administration in healthy male subjects using an untargeted metabolomics approach. All subjects received 30 mg of pioglitazone once daily in the assigned sequence and period. Urine samples were collected before pioglitazone administration and for 24 h after 7 days of administration. A total of 1465 compounds were detected and filtered using a coefficient of variance below 30% and 108 metabolites were significantly altered upon pioglitazone administration via multivariate statistical analysis. Fourteen significant metabolites were identified using authentic standards and public libraries. Additionally, pathway analysis revealed that metabolites from purine and beta-alanine metabolisms were significantly altered after pioglitazone administration. Further analysis of quantification of metabolites from purine metabolism, revealed that the xanthine/hypoxanthine and uric acid/xanthine ratios were significantly decreased at post-dose. Pioglitazone-dependent endogenous metabolites and metabolic ratio indicated the potential effect of pioglitazone on the activation of PPAR and fatty acid synthesis. Additional studies involving patients are required to validate these findings.PMID:38771175 | DOI:10.1111/cts.13834

Appl Environ Microbiol. 2024 May 21:e0060024. doi: 10.1128/aem.00600-24. Online ahead of print.ABSTRACTPolycyclic tetramate macrolactams (PTMs) are bioactive natural products commonly associated with certain actinobacterial and proteobacterial lineages. These molecules have been the subject of numerous structure-activity investigations since the 1970s. New members continue to be pursued in wild and engineered bacterial strains, and advances in PTM biosynthesis suggest their outwardly simplistic biosynthetic gene clusters (BGCs) belie unexpected product complexity. To address the origins of this complexity and understand its influence on PTM discovery, we engaged in a combination of bioinformatics to systematically classify PTM BGCs and PTM-targeted metabolomics to compare the products of select BGC types. By comparing groups of producers and BGC mutants, we exposed knowledge gaps that complicate bioinformatics-driven product predictions. In sum, we provide new insights into the evolution of PTM BGCs while systematically accounting for the PTMs discovered thus far. The combined computational and metabologenomic findings presented here should prove useful for guiding future discovery.IMPORTANCEPolycyclic tetramate macrolactam (PTM) pathways are frequently found within the genomes of biotechnologically important bacteria, including Streptomyces and Lysobacter spp. Their molecular products are typically bioactive, having substantial agricultural and therapeutic interest. Leveraging bacterial genomics for the discovery of new related molecules is thus desirable, but drawing accurate structural predictions from bioinformatics alone remains challenging. This difficulty stems from a combination of previously underappreciated biosynthetic complexity and remaining knowledge gaps, compounded by a stream of yet-uncharacterized PTM biosynthetic loci gleaned from recently sequenced bacterial genomes. We engaged in the following study to create a useful framework for cataloging historic PTM clusters, identifying new cluster variations, and tracing evolutionary paths for these molecules. Our data suggest new PTM chemistry remains discoverable in nature. However, our metabolomic and mutational analyses emphasize the practical limitations of genomics-based discovery by exposing hidden complexity.PMID:38771054 | DOI:10.1128/aem.00600-24

J Sci Food Agric. 2024 May 21. doi: 10.1002/jsfa.13607. Online ahead of print.ABSTRACTBACKGROUND: Mycotoxin surveys play an essential role in our food safety system. The obtained occurrence data form the basis for the assessment of the exposure of humans and animals to these toxic fungal secondary metabolites. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has become the gold standard for mycotoxin determination because it enables selective and sensitive multi-toxin analysis. Simultaneous determination of several hundreds of secondary fungal metabolites is feasible using this technique. In this study, we combined a targeted dilute-and-shoot LC-MS/MS-based multi-analyte approach with multivariate statistics for the analysis of Austrian wheat from two different years and different geographical origins.RESULTS: We quantified 47 secondary fungal metabolites, including regulated emerging and masked mycotoxins. The resulting multi-mycotoxin occurrence data were further analyzed using both multivariate and univariate statistics. Principal component analysis (PCA) and analysis of variance (ANOVA) simultaneous component analysis (ASCA) were employed to identify regional and yearly trends within the dataset and to quantify the variance in metabolite occurrence attributed to the different effects. In addition, secondary fungal metabolites significantly impacted by these factors were selected via ANOVA. Of the 47 secondary metabolites identified, 39 were affected by the year, region or a combined effect. Moreover, our findings show that 43 of the secondary fungal metabolites were significantly influenced by the weather conditions.CONCLUSION: The results presented in this study underline the added value of combining targeted LC-MS/MS with multivariate statistics for monitoring a broad spectrum of secondary fungal metabolites in food crops. Through multivariate statistics, trends associated with the year or region can be readily studied. The approach presented could pave the way for a better understanding of the impact of climate change on plant pathogenic fungi and its implications for food safety. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.PMID:38770945 | DOI:10.1002/jsfa.13607

J Cell Physiol. 2024 May 21. doi: 10.1002/jcp.31293. Online ahead of print.ABSTRACTThe sorting and assembly machinery (SAM) Complex is responsible for assembling β-barrel proteins in the mitochondrial membrane. Comprising three subunits, Sam35, Sam37, and Sam50, the SAM complex connects the inner and outer mitochondrial membranes by interacting with the mitochondrial contact site and cristae organizing system complex. Sam50, in particular, stabilizes the mitochondrial intermembrane space bridging (MIB) complex, which is crucial for protein transport, respiratory chain complex assembly, and regulation of cristae integrity. While the role of Sam50 in mitochondrial structure and metabolism in skeletal muscle remains unclear, this study aims to investigate its impact. Serial block-face-scanning electron microscopy and computer-assisted 3D renderings were employed to compare mitochondrial structure and networking in Sam50-deficient myotubes from mice and humans with wild-type (WT) myotubes. Furthermore, autophagosome 3D structure was assessed in human myotubes. Mitochondrial metabolic phenotypes were assessed using Gas Chromatography-Mass Spectrometry-based metabolomics to explore differential changes in WT and Sam50-deficient myotubes. The results revealed increased mitochondrial fragmentation and autophagosome formation in Sam50-deficient myotubes compared to controls. Metabolomic analysis indicated elevated metabolism of propanoate and several amino acids, including ß-Alanine, phenylalanine, and tyrosine, along with increased amino acid and fatty acid metabolism in Sam50-deficient myotubes. Furthermore, impairment of oxidative capacity was observed upon Sam50 ablation in both murine and human myotubes, as measured with the XF24 Seahorse Analyzer. Collectively, these findings support the critical role of Sam50 in establishing and maintaining mitochondrial integrity, cristae structure, and mitochondrial metabolism. By elucidating the impact of Sam50-deficiency, this study enhances our understanding of mitochondrial function in skeletal muscle.PMID:38770789 | DOI:10.1002/jcp.31293

Brief Bioinform. 2024 Mar 27;25(3):bbae240. doi: 10.1093/bib/bbae240.ABSTRACTPolygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking into account numerous genetic variations. This article provides an overview of the concept of Polygenic Risk Scores (PRS). We elucidate the historical advancements of PRS, their advantages and shortcomings in comparison with other predictive methods, and discuss their conceptual limitations in light of the complexity of biological systems. Furthermore, we provide a survey of published tools for computing PRS and associated resources. The various tools and software packages are categorized based on their technical utility for users or prospective developers. Understanding the array of available tools and their limitations is crucial for accurately assessing and predicting disease risks, facilitating early interventions, and guiding personalized healthcare decisions. Additionally, we also identify potential new avenues for future bioinformatic analyzes and advancements related to PRS.PMID:38770718 | DOI:10.1093/bib/bbae240

Adv Neurotoxicol. 2024;11:105-132. doi: 10.1016/bs.ant.2024.02.001. Epub 2024 Feb 15.ABSTRACTParkinson's Disease (PD) is a progressive neurodegenerative disease characterized by loss of dopaminergic neurons in substantia nigra pars compacta (SNpc). Iron (Fe)-dependent programmed cell death known as ferroptosis, plays a crucial role in the etiology and progression of PD. Since SNpc is particularly vulnerable to Fe toxicity, a central role for ferroptosis in the etiology and progression of PD is envisioned. Ferroptosis, characterized by reactive oxygen species (ROS)-dependent accumulation of lipid peroxides, is tightly regulated by a variety of intracellular metabolic processes. Moreover, the recently characterized bi-directional interactions between ferroptosis and the gut microbiota, not only provides another window into the mechanistic underpinnings of PD but could also suggest novel interventions in this devastating disease. Here, following a brief discussion of PD, we focus on how our expanding knowledge of Fe-induced ferroptosis and its interaction with the gut microbiota may contribute to the pathophysiology of PD and how this knowledge may be exploited to provide novel interventions in PD.PMID:38770370 | PMC:PMC11105119 | DOI:10.1016/bs.ant.2024.02.001

Heliyon. 2024 May 9;10(10):e30828. doi: 10.1016/j.heliyon.2024.e30828. eCollection 2024 May 30.ABSTRACTModified Jiawei Juanbi decoction (MJD) is used for the treatment of early-stage knee osteoarthritis (KOA). Here, modified Jiawei Juanbi decoction (MJD) was employed for the treatment of early-stage knee osteoarthritis (KOA) and its mechanisms were assessed via metabonomics and network pharmacology. A total of 24 male Sprague-Dawley rats were randomly allocated into a normal control group, a model group, and an MJD group (n = 8 rats per group). Each rat group was further equally divided into two subgroups for investigation for either 14 or 28 days. A rat model of early-stage KOA was constructed and rats were treated with MJD. Effects were evaluated based on changes in knee circumference, mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL). We also analyzed histopathological changes in articular cartilage. High-resolution mass spectrometry was used to analyze the chemical profile of MJD, identifying 228 components. Using an LC-Q-TOF-MS metabonomics approach, 33 differential metabolites were identified. The relevant pathways significantly associated with MJD include arginine and proline metabolism, vitamin B6 metabolism, as well as the biosynthesis of phenylalanine, tyrosine and tryptophan. The system pharmacology paradigm revealed that MJD contains 1027 components and associates with 1637 genes, of which 862 disease genes are related to osteoarthritis. The construction of the MJD composition-target-KOA network revealed a total of 140 intersection genes. A total of 39 hub genes were identified via integration of betweenness centrality values greater than 100 using CytoHubba. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed several significantly affected signaling pathways including the HIF-1, AGE-RAGE (in diabetic complications), IL-17, rheumatoid arthritis and TNF pathways. Integrated-omics and network pharmacology approaches revealed a necessity for further detailed investigation focusing on two major targets, namely NOS2 and NOS3, along with their essential metabolite (arginine) and associated pathways (HIF-1 signaling and arginine and proline metabolism). Real-time PCR validated significantly greater downregulation of NOS2 and HIF-1ɑ in the MJD as compared to the model group. Molecular docking analysis further confirmed the binding of active MJD with key active components. Our findings elucidate the impact of MJD on relevant pathophysiological and metabolic networks relevant to KOA and assess the drug efficacy of MJD and its underlying mechanisms of action.PMID:38770333 | PMC:PMC11103480 | DOI:10.1016/j.heliyon.2024.e30828

iScience. 2024 Apr 26;27(6):109830. doi: 10.1016/j.isci.2024.109830. eCollection 2024 Jun 21.ABSTRACTThe liver X receptor (LXR) is considered a therapeutic target for atherosclerosis treatment, but synthetic LXR agonists generally also cause hepatic steatosis and hypertriglyceridemia. Desmosterol, a final intermediate in cholesterol biosynthesis, has been identified as a selective LXR ligand that suppresses inflammation without inducing lipogenesis. Δ24-Dehydrocholesterol reductase (DHCR24) converts desmosterol into cholesterol, and we previously showed that the DHCR24 inhibitor SH42 increases desmosterol to activate LXR and attenuate experimental peritonitis and metabolic dysfunction-associated steatotic liver disease. Here, we aimed to evaluate the effect of SH42 on atherosclerosis development in APOE∗3-Leiden.CETP mice and low-density lipoproteins (LDL) receptor knockout mice, models for lipid- and inflammation-driven atherosclerosis, respectively. In both models, SH42 increased desmosterol without affecting plasma lipids. While reducing liver lipids in APOE∗3-Leiden.CETP mice, and regulating populations of circulating monocytes in LDL receptor knockout mice, SH42 did not attenuate atherosclerosis in either model.PMID:38770137 | PMC:PMC11103367 | DOI:10.1016/j.isci.2024.109830